Purpose This study investigates the complex relationship between a country's national innovation system and levels of corruption, focusing on both corruption control (CC) and corruption perception. Design/methodology/approach Using Global Innovation Index data (2013–2022) and machine learning algorithms (random forest, partial dependence plots), the study evaluates the role of innovation-related factors. Findings Political stability and regulatory quality are the strongest predictors of CC and perception. R&D investments are particularly important in upper-middle-income countries, while infrastructure plays a key role in low-income countries. Practical implications Policymakers should foster balanced innovation systems, strengthen governance, and adapt anti-corruption strategies to national income levels. Originality/value This is among the first studies applying machine learning to explore the innovation–corruption nexus across income contexts, offering novel insights for governance and sustainable development.
Köseoğlu et al. (Thu,) studied this question.